AIS Data-Guided Geolocation Correction Method for Low-Orbit Satellite Remote Sensing Imagery

Accurate geolocation of maritime objects in satellite imagery is challenging due to geometric distortions, atmospheric conditions, and sensor inaccuracies in low-earth orbit satellites. This study presents a novel AIS data-guided geolocation correction method that integrates real-time Automatic Identification System (AIS) data with satellite imagery to rectify geolocation errors. The approach utilizes the GeoAISNet neural network, which enhances positional accuracy without relying on Ground Control Points (GCPs). By incorporating a modified YOLOv8 architecture with orientation parameters and the Convolutional Block Attention Module (CBAM), detection performance improved significantly, achieving precision, recall, and F1 scores of 91.82%, 89.56%, and 90.67%, respectively. Ablation studies demonstrated the crucial impact of feature integration and attention mechanisms. Results indicate a mean Average Precision (mAP) of 89%, with General Cargo Ships achieving 99.9% AP50. Localization accuracy saw a notable improvement, with RMSE reduced from 12 meters to 3 meters, and layer normalization further enhanced stability, increasing precision, recall, and F1 scores to 94.23%, 92.67%, and 93.44%, respectively. The use of differential AIS data decreased maximum positional errors by 30%, achieving errors around 2 meters. Computational efficiency was also enhanced, with processing time reduced from 2 seconds to 0.5 seconds per image. This method effectively addresses oil spills and non-AIS vessel detection, expanding maritime surveillance capabilities. The global training dataset, validated with data from the South China Sea, ensures the method's applicability across diverse conditions.